{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import json\n",
    "\n",
    "BASE_DIR = Path(\"../dataset/a0910\")\n",
    "\n",
    "data = []\n",
    "with open(BASE_DIR / \"readme.txt\", \"r\") as f:\n",
    "    for line in f:\n",
    "        t = line.strip().split()[1:]\n",
    "        t[0] = t[0].strip(\":\")\n",
    "        data.append(t)\n",
    "data = data[:-1]\n",
    "\n",
    "data_dict = {}\n",
    "for d in data:\n",
    "    data_dict[d[0].replace(\"id\", \"num\").replace(\"knolwedge\", \"knowledge\")] = int(d[1])\n",
    "\n",
    "with open(BASE_DIR / \"describe.json\", \"w\") as f:\n",
    "    json.dump(data_dict, f, indent=4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Junyi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36369\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "BASE_DIR = Path(\"../dataset/junyi\")\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "junyi_data = pd.read_csv(BASE_DIR / \"junyi_ProblemLog_original.csv\")\n",
    "# junyi_data = junyi_data[junyi_data[\"review_mode\"] == False]\n",
    "\n",
    "junyi_data = junyi_data[junyi_data['problem_number'] == 1]\n",
    "users_data = junyi_data['user_id'].value_counts()\n",
    "user_ids = users_data[users_data > 15].index.tolist()\n",
    "\n",
    "print(len(user_ids))\n",
    "\n",
    "from random import sample\n",
    "\n",
    "selected_user_ids = sample(user_ids, 1400)\n",
    "junyi_data = junyi_data[junyi_data['user_id'].isin(selected_user_ids)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total: 57480, Correct: 39857, Incorrect: 17623\n",
      "Correct %: 69.34064022268616, Incorrect %: 30.65935977731385\n",
      "Correct / Incorrect %: 226.16467116835955\n"
     ]
    }
   ],
   "source": [
    "total = junyi_data.shape[0]\n",
    "correct = junyi_data['correct'].sum()\n",
    "incorrect = total - correct\n",
    "print(f\"Total: {total}, Correct: {correct}, Incorrect: {incorrect}\")\n",
    "print(f\"Correct %: {correct/total*100}, Incorrect %: {incorrect/total*100}\")\n",
    "print(f\"Correct / Incorrect %: {correct/incorrect*100}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# junyi_data = junyi_data[junyi_data['problem_number'] == 1]\n",
    "\n",
    "# only keep user_id, exercise, correct\n",
    "junyi_data = junyi_data[['user_id', 'problem_number', 'exercise', 'correct']]\n",
    "\n",
    "# 给user编号\n",
    "user_dict = {}\n",
    "user_id = 1\n",
    "def get_user_id(x):\n",
    "    global user_id\n",
    "    if x not in user_dict:\n",
    "        user_dict[x] = user_id\n",
    "        user_id += 1\n",
    "    return user_dict[x]\n",
    "\n",
    "junyi_data['user_id'] = junyi_data['user_id'].apply(get_user_id)\n",
    "\n",
    "junyi_exercise = pd.read_csv(BASE_DIR / \"junyi_Exercise_table.csv\")\n",
    "junyi = junyi_data.drop_duplicates(subset=['user_id', 'exercise'], keep='first')\n",
    "# junyi = junyi_data.groupby(['user_id', 'exercise', 'problem_number']).first()\n",
    "\n",
    "# junyi = junyi.reset_index()\n",
    "junyi = junyi.drop(columns=['problem_number'])\n",
    "\n",
    "train_ratio = 0.9 * 0.8\n",
    "valid_ratio = 0.1\n",
    "\n",
    "# 给exercise编号\n",
    "exercise_dict = {}\n",
    "exercise_id = 1\n",
    "def get_exercise_id(x):\n",
    "    global exercise_id\n",
    "    if x not in exercise_dict:\n",
    "        exercise_dict[x] = exercise_id\n",
    "        exercise_id += 1\n",
    "    return exercise_dict[x]\n",
    "\n",
    "junyi['exercise'] = junyi['exercise'].apply(get_exercise_id)\n",
    "junyi = junyi.rename(columns={'user_id': 'user_id', 'exercise': 'item_id', 'correct': 'score'})\n",
    "\n",
    "# 给topic编号\n",
    "topic_dict = {}\n",
    "topic_id = 1\n",
    "def get_topic_id(x):\n",
    "    global topic_id\n",
    "    if x not in topic_dict:\n",
    "        topic_dict[x] = topic_id\n",
    "        topic_id += 1\n",
    "    return f\"[{topic_dict[x]}]\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1494</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3321</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  item_id  score\n",
       "236         1        1   True\n",
       "248         2        2   True\n",
       "651         3        3   True\n",
       "1494        4        4   True\n",
       "3321        5        5  False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "junyi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "exercise_topic_df = junyi_exercise[['name', 'topic']].copy()\n",
    "exercise_topic_df['topic'] = exercise_topic_df['topic'].apply(get_topic_id)\n",
    "exercise_topic_df['name'] = exercise_topic_df['name'].apply(get_exercise_id)\n",
    "\n",
    "exercise_topic_df = exercise_topic_df.rename(columns={'name': 'item_id', 'topic': 'knowledge_code'})\n",
    "\n",
    "exercise_topic_df.to_csv(BASE_DIR / \"item.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1400/1400 [00:00<00:00, 2111.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1400 680 40701 5085 11694\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "data = junyi\n",
    "train_logs, valid_logs, test_logs = [], [], []\n",
    "\n",
    "for user_id in tqdm(data['user_id'].unique()):\n",
    "    user_data = data[data['user_id'] == user_id]\n",
    "    user_data = user_data.to_dict(orient='records')\n",
    "    train_logs += user_data[: int(train_ratio * len(user_data))]\n",
    "    valid_logs += user_data[int(train_ratio * len(user_data)): int(train_ratio * len(user_data)) + int(valid_ratio * len(user_data))]\n",
    "    test_logs += user_data[int(train_ratio * len(user_data)) + int(valid_ratio * len(user_data)):]\n",
    "\n",
    "train_logs = pd.DataFrame(train_logs)\n",
    "valid_logs = pd.DataFrame(valid_logs)\n",
    "test_logs = pd.DataFrame(test_logs)\n",
    "\n",
    "train_logs.columns = ['user_id', 'item_id', 'score']\n",
    "valid_logs.columns = ['user_id', 'item_id', 'score']\n",
    "test_logs.columns = ['user_id', 'item_id', 'score']\n",
    "\n",
    "train_logs.to_csv(BASE_DIR / \"train.csv\", index=False)\n",
    "valid_logs.to_csv(BASE_DIR / \"valid.csv\", index=False)\n",
    "test_logs.to_csv(BASE_DIR / \"test.csv\", index=False)\n",
    "\n",
    "print(len(junyi['user_id'].unique()), len(junyi['item_id'].unique()), len(train_logs), len(valid_logs), len(test_logs))\n",
    "\n",
    "with open(BASE_DIR / \"describe.json\", \"w\") as f:\n",
    "    import json\n",
    "    json.dump({\n",
    "        'user_num': len(user_dict),\n",
    "        'item_num': len(exercise_dict),\n",
    "        'knowledge_num': len(topic_dict),\n",
    "    }, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ASSISTments 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "\n",
    "BASE_DIR = Path(\"../dataset/assistment-2017\")\n",
    "\n",
    "data = pd.read_csv(BASE_DIR / \"data.csv\", low_memory=False)\n",
    "\n",
    "data = data[data['original'] == 1]\n",
    "data = data[data[\"hint\"] == 0]\n",
    "\n",
    "# 依据studentId和problemId去重, 保留第一次做题的记录\n",
    "data = data.drop_duplicates(subset=['studentId', 'problemId'], keep='first')\n",
    "\n",
    "user_count = data['studentId'].value_counts()\n",
    "user_ids = user_count[user_count > 15].index.tolist()\n",
    "\n",
    "data = data[data['studentId'].isin(user_ids)]\n",
    "\n",
    "data.to_csv(BASE_DIR / \"first_question.csv\", index=False)\n",
    "\n",
    "data = data[['studentId', 'skill', 'correct', 'problemId']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total: 131769, Correct: 68910, Incorrect: 62859\n"
     ]
    }
   ],
   "source": [
    "# 统计正确率\n",
    "total = data.shape[0]\n",
    "correct = data['correct'].sum()\n",
    "incorrect = total - correct\n",
    "print(f\"Total: {total}, Correct: {correct}, Incorrect: {incorrect}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(f\"Before : {len(data)}\")\n",
    "# user_count = data['studentId'].value_counts()\n",
    "# user_ids = user_count[user_count > 15].values.tolist()\n",
    "\n",
    "# data = data[data['studentId'].isin(user_ids)]\n",
    "# print(f\"After: {len(data)}\")\n",
    "\n",
    "problem_dict = {}\n",
    "problem_id = 1\n",
    "def get_problem_id(x):\n",
    "    global problem_id\n",
    "    if x not in problem_dict:\n",
    "        problem_dict[x] = problem_id\n",
    "        problem_id += 1\n",
    "    return problem_dict[x]\n",
    "data['problemId'] = data['problemId'].apply(get_problem_id)\n",
    "\n",
    "skill_dict = {}\n",
    "skill_id = 1\n",
    "def get_skill_id(x):\n",
    "    global skill_id\n",
    "    if x not in skill_dict:\n",
    "        skill_dict[x] = skill_id\n",
    "        skill_id += 1\n",
    "    return skill_dict[x]\n",
    "data['skill'] = data['skill'].apply(get_skill_id)\n",
    "\n",
    "user_dict = {}\n",
    "user_id = 1\n",
    "def get_user_id(x):\n",
    "    global user_id\n",
    "    if x not in user_dict:\n",
    "        user_dict[x] = user_id\n",
    "        user_id += 1\n",
    "    return user_dict[x]\n",
    "data['studentId'] = data['studentId'].apply(get_user_id)\n",
    "\n",
    "data['studentId'] = data['studentId'].astype(int)\n",
    "data['problemId'] = data['problemId'].astype(int)\n",
    "data['skill'] = data['skill'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "item_data = data[['problemId', 'skill']]\n",
    "# item_data = item_data.drop_duplicates(subset=['problemId', 'skill'], keep='first')\n",
    "item_dict = {}\n",
    "\n",
    "for index, row in item_data.iterrows():\n",
    "    pid = row['problemId']\n",
    "    sid = row['skill']\n",
    "    if pid not in item_dict:\n",
    "        item_dict[pid] = [sid]\n",
    "    else:\n",
    "        item_dict[pid].append(sid)\n",
    "\n",
    "for k, v in item_dict.items():\n",
    "    item_dict[k] = list(set(v))\n",
    "\n",
    "item_data = pd.DataFrame(item_dict.items(), columns=['item_id', 'knowledge_code'])\n",
    "item_data['knowledge_code'] = item_data['knowledge_code'].apply(lambda x: f\"[{','.join(map(str, x))}]\")\n",
    "\n",
    "item_data.to_csv(BASE_DIR / \"item.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1493/1493 [00:00<00:00, 1896.99it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "data = data[['studentId', 'problemId', 'correct']]\n",
    "data = data.rename(columns={'studentId': 'user_id', 'problemId': 'item_id', 'correct': 'score'})\n",
    "\n",
    "train_ratio = 0.9 * 0.8\n",
    "valid_ratio = 0.1\n",
    "\n",
    "train_logs, valid_logs, test_logs = [], [], []\n",
    "\n",
    "for user_id in tqdm(data['user_id'].unique()):\n",
    "    user_data = data[data['user_id'] == user_id]\n",
    "    user_data = user_data.to_dict(orient='records')\n",
    "    train_logs += user_data[: int(train_ratio * len(user_data))]\n",
    "    valid_logs += user_data[int(train_ratio * len(user_data)): int(train_ratio * len(user_data)) + int(valid_ratio * len(user_data))]\n",
    "    test_logs += user_data[int(train_ratio * len(user_data)) + int(valid_ratio * len(user_data)):]\n",
    "\n",
    "train_logs = pd.DataFrame(train_logs)\n",
    "valid_logs = pd.DataFrame(valid_logs)\n",
    "test_logs = pd.DataFrame(test_logs)\n",
    "\n",
    "train_logs.columns = ['user_id', 'item_id', 'score']\n",
    "valid_logs.columns = ['user_id', 'item_id', 'score']\n",
    "test_logs.columns = ['user_id', 'item_id', 'score']\n",
    "\n",
    "train_logs.to_csv(BASE_DIR / \"train.csv\", index=False)\n",
    "valid_logs.to_csv(BASE_DIR / \"valid.csv\", index=False)\n",
    "test_logs.to_csv(BASE_DIR / \"test.csv\", index=False)\n",
    "\n",
    "with open(BASE_DIR / \"describe.json\", \"w\") as f:\n",
    "    import json\n",
    "    json.dump({\n",
    "        'user_num': len(data['user_id'].unique()),\n",
    "        'item_num': len(data['item_id'].unique()),\n",
    "        'knowledge_num': len(item_data['knowledge_code'].unique()),\n",
    "    }, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NIPS2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from random import sample\n",
    "\n",
    "BASE_DIR = Path(\"../dataset/NIPS2020\")\n",
    "\n",
    "data1 = pd.read_csv(BASE_DIR / \"train_data\" / \"train_task_1_2.csv\")[['QuestionId', 'UserId', 'IsCorrect']]\n",
    "#data2 = pd.read_csv(BASE_DIR / \"train_data\" / \"train_task_3_4.csv\")[['QuestionId', 'UserId', 'IsCorrect']]\n",
    "#data = pd.concat([data1, data2])\n",
    "data = data1\n",
    "\n",
    "question1 = pd.read_csv(BASE_DIR / \"metadata\" / \"question_metadata_task_1_2.csv\")[['QuestionId', 'SubjectId']]\n",
    "#question2 = pd.read_csv(BASE_DIR / \"metadata\" / \"question_metadata_task_3_4.csv\")[['QuestionId', 'SubjectId']]\n",
    "#question = pd.concat([question1, question2])\n",
    "question = question1\n",
    "question = question.rename(columns={'QuestionId': 'item_id', 'SubjectId': 'knowledge_code'})\n",
    "\n",
    "data = data.rename(columns={'UserId': 'user_id', 'QuestionId': 'item_id', 'IsCorrect': 'score'})\n",
    "# 调整列的顺序\n",
    "data = data[['user_id', 'item_id', 'score']]\n",
    "\n",
    "data = data.drop_duplicates(subset=['user_id', 'item_id'], keep='first')\n",
    "\n",
    "# 筛选出做题次数大于20次的用户\n",
    "users_data = data['user_id'].value_counts()\n",
    "user_ids = users_data[users_data > 20].index.tolist()\n",
    "user_ids = sample(user_ids, 1000)\n",
    "data = data[data['user_id'].isin(user_ids)]\n",
    "\n",
    "question_ids = set(data[\"item_id\"].unique().tolist())\n",
    "question = question[question['item_id'].isin(question_ids)]\n",
    "\n",
    "knowledge = pd.read_csv(BASE_DIR / \"metadata\" / \"subject_metadata.csv\")\n",
    "knowledge_ids = set(knowledge['SubjectId'].values.tolist())\n",
    "\n",
    "# knowledge_parent_ids = knowledge['ParentId'].values.tolist()\n",
    "# knowledge_parent_ids = set(knowledge_parent_ids) \n",
    "\n",
    "# knowledge_ids = knowledge_ids - knowledge_parent_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 1000\n",
      "21858 21858\n",
      "356 356\n"
     ]
    }
   ],
   "source": [
    "user_dict = {}\n",
    "user_id = 1\n",
    "def get_user_id(x):\n",
    "    global user_id\n",
    "    if x not in user_dict:\n",
    "        user_dict[x] = user_id\n",
    "        user_id += 1\n",
    "    return user_dict[x]\n",
    "data['user_id'] = data['user_id'].apply(get_user_id)\n",
    "\n",
    "item_dict = {}\n",
    "item_id = 1\n",
    "def get_item_id(x):\n",
    "    global item_id\n",
    "    if x not in item_dict:\n",
    "        item_dict[x] = item_id\n",
    "        item_id += 1\n",
    "    return item_dict[x]\n",
    "data['item_id'] = data['item_id'].apply(get_item_id)\n",
    "question['item_id'] = question['item_id'].apply(get_item_id)\n",
    "\n",
    "item_data = question.copy()\n",
    "\n",
    "knowledge_dict = {}\n",
    "knowledge_id = 1\n",
    "for index, row in item_data.iterrows():\n",
    "    ks = []\n",
    "    for k in eval(row['knowledge_code']):\n",
    "        if k not in knowledge_ids:\n",
    "            #print(f\"Warning: {k} not in knowledge_ids\")\n",
    "            continue\n",
    "        if k not in knowledge_dict:\n",
    "            knowledge_dict[k] = knowledge_id\n",
    "            knowledge_id += 1\n",
    "        ks.append(knowledge_dict[k])\n",
    "    if len(ks) == 0:\n",
    "        print(f\"Warning: {row['knowledge_code']} has no knowledge\")\n",
    "    item_data.at[index, 'knowledge_code'] = f\"[{','.join(map(str, ks))}]\"\n",
    "\n",
    "item_data.to_csv(BASE_DIR / \"item.csv\", index=False)\n",
    "\n",
    "train_ratio = 0.9 * 0.8\n",
    "valid_ratio = 0.1\n",
    "\n",
    "print(max(user_dict.values()), len(user_dict))\n",
    "print(max(item_dict.values()), len(item_dict))\n",
    "print(max(knowledge_dict.values()), len(knowledge_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>133711.000000</td>\n",
       "      <td>133711.000000</td>\n",
       "      <td>133711.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>382.342979</td>\n",
       "      <td>7354.986680</td>\n",
       "      <td>0.654965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>269.735152</td>\n",
       "      <td>5315.927373</td>\n",
       "      <td>0.475382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>155.000000</td>\n",
       "      <td>2936.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>331.000000</td>\n",
       "      <td>6337.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>592.000000</td>\n",
       "      <td>10936.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>21858.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id        item_id          score\n",
       "count  133711.000000  133711.000000  133711.000000\n",
       "mean      382.342979    7354.986680       0.654965\n",
       "std       269.735152    5315.927373       0.475382\n",
       "min         1.000000       1.000000       0.000000\n",
       "25%       155.000000    2936.000000       0.000000\n",
       "50%       331.000000    6337.000000       1.000000\n",
       "75%       592.000000   10936.000000       1.000000\n",
       "max      1000.000000   21858.000000       1.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:00<00:00, 1594.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 21858 95805 12931 24975\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "train_logs, valid_logs, test_logs = [], [], []\n",
    "\n",
    "for user_id in tqdm(data['user_id'].unique()):\n",
    "    user_data = data[data['user_id'] == user_id]\n",
    "    user_data = user_data.to_dict(orient='records')\n",
    "    train_logs += user_data[: int(train_ratio * len(user_data))]\n",
    "    valid_logs += user_data[int(train_ratio * len(user_data)): int(train_ratio * len(user_data)) + int(valid_ratio * len(user_data))]\n",
    "    test_logs += user_data[int(train_ratio * len(user_data)) + int(valid_ratio * len(user_data)):]\n",
    "\n",
    "train_logs = pd.DataFrame(train_logs)\n",
    "valid_logs = pd.DataFrame(valid_logs)\n",
    "test_logs = pd.DataFrame(test_logs)\n",
    "\n",
    "train_logs.columns = ['user_id', 'item_id', 'score']\n",
    "valid_logs.columns = ['user_id', 'item_id', 'score']\n",
    "test_logs.columns = ['user_id', 'item_id', 'score']\n",
    "\n",
    "train_logs.to_csv(BASE_DIR / \"train.csv\", index=False)\n",
    "valid_logs.to_csv(BASE_DIR / \"valid.csv\", index=False)\n",
    "test_logs.to_csv(BASE_DIR / \"test.csv\", index=False)\n",
    "\n",
    "print(len(data['user_id'].unique()), len(data['item_id'].unique()), len(train_logs), len(valid_logs), len(test_logs))\n",
    "\n",
    "with open(BASE_DIR / \"describe.json\", \"w\") as f:\n",
    "    import json\n",
    "    json.dump({\n",
    "        'user_num': len(user_dict),\n",
    "        'item_num': len(item_dict),\n",
    "        'knowledge_num': len(knowledge_dict),\n",
    "    }, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "item_id\n",
      "13    844\n",
      "41    793\n",
      "50    691\n",
      "1     688\n",
      "19    686\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "DATASET = pathlib.Path(\"../dataset/\")\n",
    "datasets = os.listdir(DATASET)\n",
    "\n",
    "SKIP_DATASETS = [\n",
    "    \"a0910\", \n",
    "    \"assistment-2017\",\n",
    "    \"NIPS2020\",\n",
    "    # \"junyi\",\n",
    "]\n",
    "\n",
    "for dataset in datasets:\n",
    "    if dataset in SKIP_DATASETS:\n",
    "        continue\n",
    "    dir = DATASET / dataset\n",
    "    train = pd.read_csv(dir / \"train.csv\")\n",
    "    data = train.copy()\n",
    "\n",
    "    # 获取对应user_id最多的item_id\n",
    "    user_count = data['item_id'].value_counts()\n",
    "    user_count.sort_values(ascending=False, inplace=True)\n",
    "    random_item_id = user_count.index[0:3].values.tolist()\n",
    "    random = data[data['item_id'].isin(random_item_id)]\n",
    "    random.to_csv(dir / \"random.csv\", index=False)\n",
    "    print(user_count.head())\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## A0910 ##\n",
      "Total: 922, Correct: 468, Incorrect: 454\n",
      "Correct %: 50.759219088937094, Incorrect %: 49.240780911062906\n",
      "Skip a0910\n",
      "\n",
      "## JUNYI\n",
      "Item Sample: [463, 584, 357, 348, 468, 305, 487, 571, 507, 22, 431, 482, 173, 368, 20, 167]\n",
      "User: 1400 -> 732\tItem: 669 -> 16\tData: 40701 -> 1019\n",
      "Total: 1019, Correct: 534, Incorrect: 485\n",
      "Correct %: 52.40431795878312, Incorrect %: 47.59568204121688\n",
      "\n",
      "## NIPS2020 ##\n",
      "Total: 1032, Correct: 525, Incorrect: 507\n",
      "Correct %: 50.872093023255815, Incorrect %: 49.127906976744185\n",
      "Skip NIPS2020\n",
      "\n",
      "## ASSISTMENT-2017 ##\n",
      "Total: 995, Correct: 430, Incorrect: 565\n",
      "Correct %: 43.21608040201005, Incorrect %: 56.78391959798995\n",
      "Skip assistment-2017\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "from random import sample\n",
    "\n",
    "DATASET = pathlib.Path(\"../dataset/\")\n",
    "datasets = os.listdir(DATASET)\n",
    "\n",
    "SKIP_DATASET = [\n",
    "    \"a0910\",\n",
    "    # \"junyi\",\n",
    "    \"assistment-2017\",\n",
    "    \"NIPS2020\",\n",
    "]\n",
    "\n",
    "for dataset in datasets:\n",
    "    dir = DATASET / dataset\n",
    "    if dataset in SKIP_DATASET:\n",
    "        random = dir / \"random.csv\"\n",
    "        if random.exists():\n",
    "            random = pd.read_csv(random)\n",
    "            score = random['score']\n",
    "            correct = score.sum()\n",
    "            total = len(score)\n",
    "            print(f\"## {dataset.upper()} ##\")\n",
    "            print(f\"Total: {total}, Correct: {correct}, Incorrect: {total - correct}\")\n",
    "            print(f\"Correct %: {correct/total*100}, Incorrect %: {(total - correct)/total*100}\")\n",
    "        print(f\"Skip {dataset}\\n\")\n",
    "        continue\n",
    "    dir = DATASET / dataset\n",
    "    train = dir / \"train.csv\"\n",
    "    data = pd.read_csv(train)\n",
    "\n",
    "    items = data['item_id'].unique()\n",
    "    item_num_origin = len(items)\n",
    "    user_num_origin = len(data['user_id'].unique())\n",
    "    data_num_origin = len(data)\n",
    "    correct_rate = data.groupby(\"item_id\")[\"score\"].mean().reindex(items)\n",
    "    correct_rate = correct_rate[correct_rate > 0.4]\n",
    "    correct_rate = correct_rate[correct_rate < 0.6]\n",
    "    select_items_from = correct_rate.index\n",
    "    #correct_rate\n",
    "    #print(select_items_from)\n",
    "    item_sample_num = int(item_num_origin / (data_num_origin / 1000))\n",
    "    sample_items = sample(list(select_items_from), item_sample_num)\n",
    "    sample_items = [int(i) for i in sample_items]\n",
    "    \n",
    "    data = data[data['item_id'].isin(sample_items)]\n",
    "    log = f\"## {dataset.upper()}\\n\"\n",
    "    log += f\"Item Sample: {sample_items}\\n\"\n",
    "    log += f\"User: {user_num_origin} -> {len(data['user_id'].unique())}\\t\"\n",
    "    log += f\"Item: {item_num_origin} -> {len(data['item_id'].unique())}\\t\"\n",
    "    log += f\"Data: {data_num_origin} -> {len(data)}\"\n",
    "    print(log)\n",
    "    data.to_csv(dir / \"random.csv\", index=False)\n",
    "\n",
    "    score = data['score']\n",
    "    correct = score.sum()\n",
    "    total = len(score)\n",
    "    print(f\"Total: {total}, Correct: {correct}, Incorrect: {total - correct}\")\n",
    "    print(f\"Correct %: {correct/total*100}, Incorrect %: {(total - correct)/total*100}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start junyi\n",
      "{1: {1, 38, 70, 41, 10, 13, 142, 240, 49, 19, 51, 91, 60, 30}, 2: {2, 34, 185, 41, 9, 13, 47, 15, 114, 50, 54, 87, 56, 89, 58, 27}, 3: {2, 3, 9, 522, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 534, 535, 24, 25, 529, 27, 28, 29, 30, 35, 36, 37, 41, 42, 554, 45, 558, 47, 48, 49, 50, 46, 52, 53, 54, 51, 56, 58, 62, 63, 64, 66, 67, 68, 70, 71, 74, 77, 79, 591, 82, 83, 84, 85, 87, 88, 89, 599, 91, 92, 526, 95, 96, 99, 100, 101, 109, 112, 114, 627, 115, 117, 118, 119, 120, 121, 122, 124, 133, 136, 652, 141, 144, 145, 147, 148, 150, 151, 154, 155, 156, 157, 159, 161, 164, 167, 172, 173, 175, 177, 180, 183, 185, 186, 187, 188, 191, 192, 193, 196, 198, 200, 201, 202, 203, 204, 205, 206, 209, 212, 214, 215, 216, 217, 223, 231, 234, 235, 236, 239, 243, 244, 245, 246, 249, 251, 252, 256, 259, 260, 261, 267, 269, 274, 275, 277, 279, 281, 291, 292, 293, 295, 297, 301, 303, 306, 308, 313, 319, 321, 325, 327, 332, 335, 338, 343, 346, 354, 358, 370, 373, 376, 382, 384, 390, 394, 395, 397, 405, 420, 433, 437, 442, 445, 446, 452, 453, 458, 459, 466, 468, 495, 498, 501, 502, 511}, 4: {2, 3, 4, 9, 13, 14, 15, 16, 17, 19, 20, 21, 24, 25, 27, 28, 29, 31, 36, 37, 38, 40, 41, 43, 44, 45, 48, 50, 562, 53, 54, 58, 60, 61, 64, 67, 70, 73, 74, 75, 77, 78, 80, 81, 83, 84, 85, 89, 91, 95, 99, 100, 101, 111, 114, 116, 117, 118, 119, 122, 128, 130, 132, 134, 142, 143, 148, 151, 152, 153, 159, 160, 161, 162, 167, 168, 171, 173, 175, 177, 180, 182, 185, 187, 188, 189, 195, 196, 203, 204, 212, 214, 220, 226, 231, 234, 235, 239, 240, 241, 242, 244, 250, 254, 259, 267, 269, 271, 272, 273, 275, 277, 281, 294, 296, 298, 301, 302, 308, 315, 316, 318, 319, 321, 329, 333, 334, 337, 342, 349, 356, 390, 391, 442, 467, 477, 498, 502}, 5: {384, 2, 5, 390, 9, 10, 269, 142, 15, 144, 16, 19, 281, 27, 29, 30, 159, 37, 38, 41, 47, 48, 49, 50, 51, 308, 56, 185, 58, 70, 71, 77, 334, 89, 95, 231, 240, 245, 117, 119, 255}, 6: {128, 1, 3, 9, 137, 11, 13, 142, 15, 16, 143, 17, 19, 21, 22, 533, 24, 155, 30, 163, 36, 294, 297, 41, 172, 301, 46, 47, 49, 50, 51, 308, 438, 55, 182, 57, 61, 64, 69, 70, 71, 333, 79, 82, 212, 89, 239, 368, 114, 245, 121, 254}, 7: {6, 9, 13, 526, 143, 16, 145, 146, 151, 27, 159, 163, 419, 171, 173, 49, 50, 438, 185, 57, 64, 581, 72, 334, 83, 238, 241, 115, 254, 383}, 8: {1, 2, 3, 5, 7, 137, 11, 13, 145, 18, 19, 17, 277, 22, 151, 24, 25, 154, 283, 155, 282, 30, 161, 34, 163, 548, 165, 294, 41, 46, 47, 50, 306, 54, 182, 313, 191, 72, 73, 77, 206, 81, 87, 88, 89, 220, 249, 368, 119, 121, 254}, 9: {129, 260, 8, 10, 267, 269, 144, 151, 537, 29, 159, 291, 168, 50, 438, 56, 185, 66, 75, 224, 108, 111, 118}, 10: {159, 258, 2, 37, 9, 202, 13, 303, 16, 47, 338, 19, 241, 50, 54, 119, 62, 223}, 11: {256, 128, 2, 1, 384, 3, 9, 10, 511, 140, 13, 269, 15, 144, 272, 145, 19, 148, 21, 278, 277, 24, 16, 17, 20, 412, 29, 152, 159, 163, 37, 38, 294, 296, 41, 171, 175, 48, 49, 177, 50, 308, 306, 52, 56, 185, 59, 60, 319, 64, 195, 196, 70, 71, 201, 82, 84, 343, 215, 87, 93, 223, 226, 99, 101, 234, 110, 240, 371, 245, 117, 120, 122, 255}, 12: {2, 34, 163, 69, 38, 137, 234, 13, 46, 143, 47, 50, 114, 55, 182, 22, 54, 89}, 13: {2, 6, 7, 9, 11, 13, 17, 18, 19, 20, 21, 22, 24, 25, 27, 29, 30, 34, 37, 41, 46, 47, 48, 49, 50, 54, 55, 56, 57, 60, 64, 66, 69, 70, 71, 72, 74, 78, 82, 86, 89, 93, 94, 99, 111, 114, 117, 118, 119, 120, 122, 133, 137, 143, 144, 145, 146, 152, 153, 154, 164, 167, 171, 173, 182, 186, 188, 195, 197, 206, 209, 216, 220, 231, 234, 239, 240, 241, 253, 263, 265, 269, 271, 272, 277, 281, 294, 295, 297, 298, 317, 318, 319, 327, 329, 337, 345, 351, 368, 391, 491, 496, 498}, 14: {128, 1, 9, 10, 11, 12, 13, 141, 15, 14, 142, 21, 26, 27, 30, 161, 165, 38, 41, 298, 171, 45, 47, 175, 49, 50, 51, 58, 60, 319, 66, 195, 196, 78, 212, 93, 95, 239, 240, 114, 118, 120, 122}, 15: {1, 297, 13, 14, 15, 47, 333, 50, 370, 20, 19, 58, 27}, 16: {2, 136, 9, 13, 14, 269, 272, 16, 19, 20, 25, 27, 29, 159, 165, 38, 40, 41, 47, 50, 180, 54, 56, 70, 76, 209, 220, 93, 351, 226, 366, 117, 127}, 17: {2, 70, 41, 77, 13, 15, 50, 51, 84, 19, 180, 20, 54, 89, 58, 27}, 18: {1, 132, 136, 9, 13, 14, 15, 16, 529, 19, 275, 153, 26, 27, 159, 164, 165, 295, 41, 47, 50, 185, 70, 75, 212, 85, 468, 93, 112, 501, 117, 118, 254}, 19: {1, 9, 10, 14, 15, 17, 19, 275, 21, 24, 27, 30, 161, 293, 165, 38, 41, 175, 47, 49, 50, 51, 58, 187, 60, 316, 65, 195, 70, 77, 84, 214, 89, 93, 95, 226, 101, 106, 109, 110, 240, 116, 122}, 20: {354, 167, 11, 171, 269, 173, 175, 176, 209, 18, 50, 84, 144, 241, 119, 56, 29, 447}, 21: {2, 99, 36, 100, 67, 104, 41, 10, 172, 12, 142, 15, 50, 19, 532, 186, 27, 95}, 22: {1, 4, 5, 6, 137, 10, 511, 13, 143, 19, 20, 21, 22, 288, 161, 163, 164, 419, 38, 36, 37, 41, 172, 46, 47, 48, 177, 49, 52, 53, 182, 54, 57, 58, 187, 60, 188, 190, 319, 64, 62, 195, 323, 69, 70, 67, 72, 329, 202, 196, 204, 589, 80, 214, 343, 87, 120, 95, 99, 356, 358, 234, 106, 240, 114, 116, 245, 376, 377, 122, 124, 255}, 23: {2, 131, 259, 137, 10, 143, 273, 22, 27, 30, 34, 37, 38, 41, 301, 46, 49, 50, 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123}, 1228: {1, 2, 67, 101, 38, 37, 70, 41, 298, 11, 172, 13, 15, 47, 20, 85, 58, 27}, 1229: {1, 37, 38, 41, 10, 13, 142, 47, 240, 51, 58, 27, 60, 30, 95}, 1230: {159, 161, 356, 229, 383, 9, 14, 146, 20, 182, 89, 187, 94, 31}, 1231: {2, 133, 40, 74, 204, 271, 113, 370, 214, 25, 91, 127}, 1232: {1, 37, 38, 70, 40, 41, 171, 13, 47, 240, 15, 114, 19, 119, 54, 87, 89, 159}, 1233: {160, 37, 263, 41, 11, 204, 173, 110, 47, 335, 241, 303, 19, 20, 58, 27}, 1234: {1, 38, 263, 40, 10, 141, 14, 175, 49, 19, 27}, 1235: {1, 2, 195, 196, 101, 10, 13, 110, 142, 240, 49, 114, 19, 24, 58, 27, 316}, 1236: {99, 359, 456, 73, 138, 142, 306, 51, 343, 282, 508}, 1237: {418, 41, 362, 10, 13, 269, 47, 19, 86, 56, 350}, 1238: {2, 258, 12, 272, 19, 537, 25, 282, 40, 41, 47, 176, 306, 435, 52, 50, 54, 325, 78, 214, 93, 226, 113, 370}, 1239: {1, 6, 71, 38, 234, 13, 47, 15, 49, 240, 241, 119, 57, 58, 27, 60, 30}, 1240: {163, 41, 9, 11, 13, 46, 143, 146, 85, 123, 542}, 1241: {1, 2, 37, 38, 70, 77, 49, 50, 19, 54, 89, 27, 30}, 1242: {1, 2, 70, 38, 13, 47, 15, 50, 19, 20, 180, 54, 87, 308, 155, 159}, 1243: {288, 323, 37, 6, 41, 11, 13, 50, 83, 85, 57}, 1244: {1, 195, 58, 37, 70, 38, 10, 13, 77, 15, 49, 51, 19, 89, 122, 60, 95}, 1245: {577, 354, 167, 522, 269, 111, 16, 437, 56, 185, 315, 159}, 1246: {1, 2, 6, 137, 11, 15, 20, 37, 38, 41, 46, 54, 57, 58, 188, 323, 206, 82, 339, 85, 235, 110, 123}, 1247: {1, 194, 37, 40, 41, 13, 14, 113, 19, 20, 350}, 1248: {34, 37, 41, 47, 15, 49, 19, 51, 87, 58, 27}, 1249: {67, 36, 9, 172, 13, 335, 303, 49, 82, 338, 20, 116, 214, 89, 94, 287}, 1250: {384, 99, 196, 133, 297, 74, 142, 271, 48, 370, 88, 58, 91, 95}, 1251: {128, 1, 2, 5, 9, 10, 13, 142, 19, 30, 37, 41, 47, 48, 49, 50, 51, 58, 195, 70, 89, 95, 99, 245}, 1252: {1, 69, 319, 41, 9, 13, 206, 143, 82, 50, 20, 89, 94, 159}, 1253: {1, 10, 13, 142, 20, 27, 30, 161, 37, 38, 41, 47, 50, 51, 53, 58, 187, 60, 62, 65, 195, 196, 70, 80, 95, 356, 120}, 1254: {1, 6, 137, 9, 267, 13, 25, 160, 419, 163, 41, 173, 46, 50, 307, 55, 57, 188, 72, 74, 206, 208, 89, 234, 241, 114, 117, 119}, 1255: {1, 2, 196, 38, 70, 41, 10, 13, 47, 240, 49, 50, 51, 19, 27, 95}, 1256: {1, 2, 38, 70, 41, 10, 13, 15, 240, 49, 47, 19, 58, 27, 30}, 1257: {1, 9, 11, 13, 19, 25, 27, 30, 36, 37, 38, 172, 49, 50, 54, 70, 89, 106, 114}, 1258: {2, 132, 281, 71, 269, 301, 13, 144, 241, 306, 111, 119, 56, 185, 155, 93, 254, 159}, 1259: {257, 193, 37, 199, 331, 11, 45, 494, 15, 50, 19, 217, 378, 91}, 1260: {1, 37, 70, 38, 11, 173, 13, 15, 241, 51, 20, 278, 119, 89, 27}, 1261: {37, 41, 11, 301, 13, 47, 110, 81, 50, 19, 114, 118, 25}, 1262: {64, 34, 37, 73, 234, 15, 176, 81, 50, 143, 93, 271, 54, 87, 22, 82, 186, 61}, 1263: {64, 1, 2, 38, 41, 13, 46, 82, 117, 182, 29}, 1264: {67, 37, 38, 41, 234, 11, 172, 13, 143, 47, 50, 82, 123, 317}, 1265: {1, 195, 37, 296, 11, 144, 16, 50, 19, 49, 117, 56, 185, 27, 29}, 1266: {1, 37, 38, 70, 91, 13, 173, 47, 241, 50, 19, 20, 309, 88, 27, 93, 31}, 1267: {2, 137, 234, 13, 110, 144, 113, 82, 22, 54, 94, 159}, 1268: {128, 1, 2, 195, 37, 70, 38, 27, 10, 13, 47, 240, 49, 50, 20, 122, 91, 95}, 1269: {1, 70, 187, 13, 15, 240, 49, 18, 19, 50, 20, 27}, 1270: {89, 41, 106, 9, 234, 13, 206, 172, 50, 19, 25, 159}, 1271: {128, 1, 384, 13, 142, 143, 19, 21, 27, 30, 34, 37, 48, 50, 51, 54, 58, 60, 61, 64, 195, 196, 70, 87, 99, 114, 245, 117, 122}, 1272: {1, 133, 397, 19, 27, 159, 35, 41, 297, 43, 52, 186, 74, 339, 91, 241, 119, 125, 127}, 1273: {64, 1, 69, 41, 234, 235, 204, 13, 15, 82, 83, 50, 123, 61, 30}, 1274: {6, 13, 21, 27, 38, 41, 46, 47, 51, 57, 58, 61, 195, 70, 82, 83, 84, 234, 240, 122}, 1275: {163, 9, 13, 173, 208, 241, 119, 89, 91, 125, 319}, 1276: {1, 37, 38, 10, 15, 240, 49, 50, 19, 20, 89, 58, 27, 30}, 1277: {129, 260, 267, 13, 20, 159, 33, 161, 38, 168, 41, 299, 46, 47, 72, 206, 467, 213, 378}, 1278: {1, 2, 10, 13, 142, 15, 17, 19, 20, 27, 31, 161, 293, 38, 41, 47, 49, 50, 51, 54, 58, 59, 187, 316, 196, 70, 202, 80, 88, 217, 101, 229, 114, 120, 122}, 1279: {322, 2, 229, 69, 170, 426, 302, 143, 82, 19, 405, 309, 88, 89, 91}, 1280: {1, 2, 137, 13, 15, 143, 18, 19, 146, 277, 24, 154, 30, 32, 34, 37, 46, 182, 55, 54, 188, 189, 61, 64, 69, 234, 235, 110, 115, 383}, 1281: {70, 38, 41, 332, 13, 47, 15, 50, 20, 60, 30}, 1282: {608, 416, 35, 612, 8, 425, 552, 242, 211, 213, 344}, 1283: {1, 2, 37, 38, 40, 9, 13, 47, 15, 19, 437, 27}, 1284: {1, 71, 10, 173, 13, 15, 16, 240, 49, 83, 51, 19, 212, 119, 89, 58, 60}, 1285: {2, 143, 15, 147, 34, 41, 173, 46, 50, 182, 55, 57, 60, 188, 82, 233, 234, 110, 244, 119}, 1286: {128, 1, 89, 163, 456, 141, 240, 115, 214, 57, 95}, 1287: {258, 290, 483, 102, 584, 169, 266, 400, 338, 23, 476, 63}, 1288: {2, 67, 106, 395, 172, 272, 114, 50, 54, 220, 93}, 1289: {265, 13, 16, 18, 154, 159, 32, 163, 46, 175, 176, 51, 182, 314, 319, 320, 64, 196, 207, 82, 85, 598, 343, 99, 228, 231, 502}, 1290: {193, 37, 72, 41, 10, 461, 46, 528, 82, 19, 50, 87, 55, 217, 27}, 1291: {2, 35, 6, 423, 13, 463, 50, 82, 628, 629, 182, 407}, 1292: {1, 2, 262, 13, 14, 15, 274, 20, 25, 27, 416, 289, 37, 38, 41, 47, 49, 50, 51, 52, 53, 58, 60, 62, 202, 332, 240, 242}, 1293: {1, 161, 70, 41, 13, 14, 15, 47, 240, 187, 20, 58, 27, 60, 30}, 1294: {1, 2, 38, 9, 13, 15, 47, 50, 19, 54, 89, 58, 60, 30}, 1295: {128, 2, 5, 13, 15, 19, 21, 159, 37, 41, 173, 47, 49, 51, 58, 60, 195, 196, 70, 99, 485, 240, 241, 119}, 1296: {2, 40, 41, 9, 13, 47, 15, 50, 19, 212, 20, 118, 89, 127}, 1297: {1, 67, 36, 11, 172, 13, 14, 271, 49, 370, 116, 20, 214, 89, 254}, 1298: {1, 37, 70, 38, 41, 13, 110, 47, 240, 15, 19, 308, 54, 89, 58, 27, 60, 30}, 1299: {99, 58, 37, 38, 70, 41, 10, 15, 48, 240, 19, 21, 122, 27, 60, 319}, 1300: {1, 2, 6, 263, 13, 144, 19, 27, 287, 160, 38, 46, 559, 50, 54, 55, 58, 61, 317, 64, 325, 72, 206, 85, 214, 218, 234, 241, 117, 119}, 1301: {1, 393, 268, 280, 284, 34, 35, 425, 52, 182, 312, 458, 463, 472, 221, 486, 114, 373, 251}, 1302: {263, 489, 74, 13, 46, 271, 113, 82, 659, 20, 533, 89, 282, 123, 317, 94, 159}, 1303: {89, 1, 37, 104, 41, 9, 11, 49, 467, 121, 58}, 1304: {35, 40, 41, 10, 13, 15, 50, 51, 20, 19, 58, 27}, 1305: {64, 2, 69, 41, 234, 11, 13, 46, 206, 143, 50, 182, 87}, 1306: {1, 2, 9, 11, 13, 19, 163, 41, 170, 49, 50, 51, 54, 185, 58, 319, 322, 195, 95, 240, 114}, 1307: {1, 196, 37, 41, 10, 13, 142, 50, 19, 180, 89, 58, 95}, 1308: {34, 37, 38, 70, 41, 13, 143, 47, 49, 19, 182, 89, 94}, 1309: {2, 37, 319, 41, 9, 13, 46, 15, 49, 81, 114, 146, 54, 89, 383}, 1310: {1, 9, 18, 19, 27, 37, 41, 298, 176, 49, 51, 54, 56, 58, 198, 89, 231, 107, 114}, 1311: {13, 15, 271, 17, 16, 19, 277, 25, 159, 41, 173, 176, 50, 54, 86, 101, 110, 241, 114}, 1312: {354, 163, 167, 9, 298, 269, 111, 117, 56, 89, 29, 319}, 1313: {1, 136, 141, 272, 17, 21, 152, 25, 282, 27, 153, 34, 308, 58, 61, 319, 195, 332, 204, 77, 99, 100, 240, 122, 511}, 1314: {2, 198, 43, 238, 81, 18, 50, 339, 82, 502, 407}, 1315: {322, 67, 2, 41, 170, 106, 234, 46, 206, 50, 114, 85, 54, 91, 159}, 1316: {71, 296, 298, 171, 269, 173, 309, 438, 119, 88, 125, 31}, 1317: {160, 97, 98, 455, 41, 138, 234, 74, 13, 173, 559, 206, 241, 498, 46, 82, 25, 282}, 1318: {1, 6, 72, 41, 234, 13, 46, 206, 49, 82, 85, 214, 287}, 1319: {263, 13, 15, 25, 27, 159, 160, 36, 37, 41, 172, 47, 50, 180, 54, 67, 70, 86, 89, 106}, 1320: {1, 2, 34, 69, 38, 70, 234, 82, 19, 51, 114, 54, 58, 60, 30}, 1321: {512, 517, 6, 393, 396, 13, 279, 38, 423, 47, 52, 312, 57, 449, 193, 461, 82, 469, 217, 221, 482, 490, 494, 381, 511}, 1322: {65, 323, 37, 199, 73, 108, 204, 397, 399, 241, 274, 339, 85}, 1323: {1, 195, 196, 12, 142, 315, 49, 50, 120, 58, 27, 95}, 1324: {64, 2, 34, 37, 72, 234, 13, 143, 50, 19, 54, 55, 87, 61, 94}, 1325: {322, 163, 37, 38, 71, 41, 11, 13, 46, 47, 241, 82, 50, 89, 61, 31}, 1326: {128, 1, 159, 195, 72, 234, 10, 47, 240, 241, 49, 82, 119, 55, 58, 27, 61, 95}, 1327: {10, 13, 19, 27, 36, 37, 38, 47, 49, 50, 54, 58, 60, 195, 70, 348, 95, 106, 240, 114}, 1328: {1, 263, 168, 269, 173, 241, 85, 86, 119, 56, 345, 383}, 1329: {1, 67, 37, 38, 27, 41, 10, 106, 172, 13, 301, 19, 116, 58, 91}, 1330: {450, 163, 34, 41, 13, 206, 81, 89, 123, 126, 319}, 1331: {89, 1, 99, 9, 41, 173, 14, 269, 144, 241, 50, 20, 119, 56, 185, 58, 27, 159}, 1332: {1, 2, 38, 331, 332, 45, 14, 15, 13, 19, 52, 180, 20, 60, 30, 127}, 1333: {9, 10, 13, 25, 27, 29, 159, 160, 37, 41, 173, 47, 49, 50, 58, 77, 89, 110, 240, 241, 117}, 1334: {128, 1, 2, 10, 13, 15, 21, 37, 47, 49, 50, 51, 308, 58, 77, 83, 89, 122, 255}, 1335: {1, 2, 37, 41, 14, 47, 49, 19, 51, 180, 20, 58, 27, 60}, 1336: {77, 269, 113, 114, 436, 276, 54, 281, 443, 540, 445, 255}, 1337: {1, 354, 11, 204, 269, 46, 13, 144, 16, 82, 206, 117, 278, 29, 159}, 1338: {1, 37, 72, 41, 234, 235, 206, 47, 143, 50, 182, 55, 27}, 1339: {89, 1, 2, 6, 41, 173, 46, 206, 111, 241, 82, 50, 146, 502, 119, 56, 57, 126}, 1340: {2, 37, 11, 47, 15, 241, 50, 19, 85, 119, 89, 30}, 1341: {1, 130, 67, 6, 107, 13, 305, 82, 116, 55, 279, 27}, 1342: {34, 234, 11, 235, 13, 47, 15, 82, 534, 123, 61}, 1343: {159, 58, 37, 169, 75, 16, 176, 50, 19, 18, 20, 154, 27, 125, 447}, 1344: {2, 37, 424, 41, 234, 364, 46, 143, 50, 404, 89, 94, 57}, 1345: {1, 2, 161, 37, 70, 187, 14, 47, 15, 50, 180, 54, 86, 27}, 1346: {1, 2, 195, 196, 101, 71, 10, 49, 114, 51, 241, 277, 54, 50, 25, 27, 95}, 1347: {35, 71, 423, 41, 9, 269, 13, 335, 77, 241, 19, 83, 309, 20, 88, 89, 91, 31}, 1348: {37, 38, 70, 41, 11, 334, 15, 47, 50, 19, 27, 30}, 1349: {1, 67, 260, 36, 41, 106, 172, 77, 366, 47, 116, 501, 85, 54, 152, 118, 27, 30}, 1350: {133, 229, 40, 41, 10, 74, 13, 113, 49, 50, 122}, 1351: {281, 296, 74, 269, 14, 271, 144, 16, 56, 25, 187, 159}, 1352: {2, 37, 70, 41, 173, 47, 241, 50, 49, 119, 58, 27}, 1353: {9, 91, 335, 303, 338, 19, 52, 20, 370, 89, 27, 31}, 1354: {2, 13, 19, 22, 159, 37, 171, 46, 47, 50, 182, 55, 54, 185, 57, 65, 69, 72, 206, 212, 89, 94, 103, 234, 114}, 1355: {1, 4, 6, 266, 269, 13, 15, 144, 282, 27, 551, 296, 175, 47, 176, 50, 566, 55, 58, 314, 320, 69, 198, 586, 82, 84, 85, 86, 234, 498, 372, 502, 122}, 1356: {74, 45, 173, 271, 13, 241, 370, 309, 88, 218, 91, 31}, 1357: {1, 2, 37, 70, 38, 10, 13, 142, 47, 50, 51, 20, 180, 27, 60}, 1358: {224, 1, 2, 513, 38, 114, 50, 54, 89, 27, 30}, 1359: {1, 34, 37, 41, 13, 46, 47, 50, 472, 58, 27}, 1360: {1, 2, 41, 15, 49, 50, 19, 273, 309, 88, 27, 63}, 1361: {416, 1, 70, 9, 41, 76, 13, 15, 47, 50, 242, 20, 370, 89}, 1362: {33, 2, 34, 37, 202, 143, 15, 399, 402, 147, 19, 274, 50, 81, 377, 348, 285, 62}, 1363: {58, 37, 41, 74, 13, 15, 47, 49, 370, 498, 52, 51, 87, 282, 27}, 1364: {69, 229, 269, 82, 18, 498, 309, 118, 91, 29, 319}, 1365: {2, 72, 41, 10, 234, 13, 206, 144, 114, 50, 182, 185, 27, 318, 159}, 1366: {2, 354, 37, 70, 13, 110, 15, 60, 50, 19, 437, 540}, 1367: {354, 2, 263, 41, 11, 13, 269, 143, 176, 145, 16, 50, 52, 84, 56, 123, 93, 159}, 1368: {185, 269, 239, 16, 81, 241, 119, 216, 313, 445, 254, 351}, 1369: {1, 37, 38, 41, 10, 47, 241, 49, 51, 20, 50, 54, 19, 27}, 1370: {1, 2, 196, 37, 38, 70, 41, 13, 142, 47, 240, 114, 50, 20, 54, 58, 27, 30}, 1371: {1, 2, 196, 263, 169, 173, 15, 114, 50, 85, 119}, 1372: {2, 37, 10, 12, 13, 142, 49, 50, 182, 58, 27, 61}, 1373: {1, 2, 323, 295, 73, 234, 397, 82, 339, 85, 26}, 1374: {1, 322, 163, 419, 37, 489, 170, 13, 110, 45, 50, 19, 51, 85, 89, 91, 31}, 1375: {1, 74, 173, 13, 145, 274, 19, 20, 241, 147, 119, 25}, 1376: {1, 195, 37, 186, 297, 41, 271, 50, 19, 21, 58, 27}, 1377: {1, 34, 69, 230, 41, 234, 364, 19, 182, 87, 184}, 1378: {1, 9, 12, 142, 15, 19, 153, 30, 37, 38, 175, 49, 50, 58, 60, 195, 196, 70, 77, 89, 95, 240}, 1379: {37, 70, 325, 383, 265, 186, 282, 13, 142, 19, 214, 89, 58, 30, 287}, 1380: {576, 74, 523, 428, 138, 208, 241, 274, 498, 119, 23, 504, 569, 347}, 1381: {1, 67, 100, 263, 106, 172, 49, 466, 50, 51, 245, 89, 58, 91, 95}, 1382: {128, 129, 1, 259, 2, 137, 10, 13, 273, 20, 405, 22, 31, 33, 161, 41, 51, 309, 54, 437, 195, 70, 199, 72, 206, 88, 91, 94, 95, 229, 234, 240, 378, 379}, 1383: {128, 1, 9, 10, 11, 13, 15, 145, 278, 30, 37, 165, 41, 47, 49, 51, 58, 318, 195, 70, 220, 240, 122}, 1384: {67, 36, 74, 11, 172, 13, 46, 271, 206, 81, 498, 237, 85, 282, 123, 157}, 1385: {128, 2, 196, 122, 10, 13, 49, 50, 51, 20, 19, 54, 89, 58, 30}, 1386: {37, 12, 173, 46, 143, 16, 528, 50, 530, 20, 185, 94}, 1387: {34, 100, 69, 7, 141, 109, 47, 82, 19, 306, 182, 55}, 1388: {37, 137, 41, 13, 45, 15, 46, 50, 19, 82, 87}, 1389: {1, 37, 72, 234, 13, 206, 241, 50, 87, 119, 57}, 1390: {2, 4, 40, 397, 207, 16, 176, 50, 339, 85, 88, 185, 91, 159}, 1391: {1, 2, 37, 13, 45, 47, 15, 49, 50, 19, 180, 27}, 1392: {1, 2, 37, 38, 295, 72, 41, 13, 46, 15, 47, 49, 50, 115, 51, 19}, 1393: {1, 70, 41, 11, 13, 47, 15, 19, 437, 30, 27, 318}, 1394: {38, 70, 41, 9, 13, 47, 15, 50, 19, 20, 89}, 1395: {37, 41, 12, 13, 173, 47, 49, 50, 241, 113, 27}, 1396: {224, 1, 513, 72, 409, 13, 206, 81, 82, 55, 57, 378}, 1397: {2, 9, 13, 173, 14, 241, 19, 20, 89, 27, 159}, 1398: {1, 67, 36, 41, 11, 172, 15, 19, 116, 85, 118, 89}, 1399: {1, 2, 37, 41, 173, 206, 303, 15, 13, 338, 50, 46}, 1400: {322, 163, 41, 91, 170, 301, 173, 79, 241, 82, 88, 89, 123}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "junyi: 100%|██████████| 936600/936600 [00:26<00:00, 35770.01it/s]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4097558/2553949119.py:71: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  result = pd.concat([result, temp_df], ignore_index=True)\n",
      "/tmp/ipykernel_4097558/2553949119.py:71: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  result = pd.concat([result, temp_df], ignore_index=True)\n",
      "junyi: 100%|█████████▉| 935931/936600 [00:19<00:00, 32905.84it/s] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finish junyi\n"
     ]
    }
   ],
   "source": [
    "# 题目是否真实曝光给了学生\n",
    "\n",
    "import pathlib\n",
    "import os\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import concurrent.futures\n",
    "\n",
    "cpu_cores = os.cpu_count() - 2\n",
    "\n",
    "DATASET = pathlib.Path(\"../dataset/\")\n",
    "datasets = os.listdir(DATASET)\n",
    "\n",
    "SKIP_DATASET = [\n",
    "    \"a0910\",\n",
    "    # \"junyi\",\n",
    "    \"assistment-2017\",\n",
    "    \"NIPS2020\",\n",
    "]\n",
    "\n",
    "for dataset in datasets:\n",
    "    dir = DATASET / dataset\n",
    "    expose = dir / \"train-expose.csv\"\n",
    "    if dataset in SKIP_DATASET:\n",
    "        continue\n",
    "    train = dir / \"train.csv\"\n",
    "    data = pd.read_csv(train)\n",
    "    data[\"exposure\"] = 1.0\n",
    "    data[\"score\"] = data[\"score\"].astype(float)\n",
    "    \n",
    "    all_items = data[\"item_id\"].unique().tolist()\n",
    "    user_ids = data[\"user_id\"].unique().tolist()\n",
    "\n",
    "    data[\"index\"] = data.index\n",
    "    \n",
    "    print(f\"Start {dataset}\")\n",
    "    user_with_items = {}\n",
    "    for user_id in user_ids:\n",
    "        user_with_items[user_id] = set(data[data[\"user_id\"] == user_id][\"item_id\"].tolist())\n",
    "\n",
    "    print(user_with_items)\n",
    "\n",
    "    # data = data.set_index([\"user_id\", \"item_id\"])\n",
    "    def add_zero_expose(user_id):\n",
    "        temp_data = []\n",
    "        for item_id in all_items:\n",
    "            if item_id not in user_with_items[user_id]:\n",
    "                temp_data.append([user_id, item_id, 0.0, 0.0])\n",
    "        user_df = data[data[\"user_id\"] == user_id]\n",
    "        temp_df = pd.DataFrame(temp_data, columns=[\"user_id\", \"item_id\", \"score\", \"exposure\"])\n",
    "        temp_df = pd.concat([user_df, temp_df], ignore_index=True)\n",
    "        temp_df[\"index\"] = temp_df.index\n",
    "        return temp_df\n",
    "\n",
    "    bar = tqdm(total=len(all_items) * len(user_ids), desc=f\"{dataset}: \")\n",
    "\n",
    "    worker_users = []\n",
    "    for cpu_id in range(cpu_cores):\n",
    "        worker_users.append(user_ids[cpu_id::cpu_cores])\n",
    "    worker_index = 0\n",
    "\n",
    "    result = pd.DataFrame(columns=[\"index\", \"user_id\", \"item_id\", \"score\", \"exposure\"])\n",
    "    \n",
    "    with concurrent.futures.ThreadPoolExecutor(max_workers=cpu_cores) as executor:\n",
    "        futures = []\n",
    "        for user_id in user_ids:\n",
    "            futures.append(executor.submit(add_zero_expose, user_id))\n",
    "        for future in concurrent.futures.as_completed(futures):\n",
    "            # lock\n",
    "            temp_df = future.result()\n",
    "            result = pd.concat([result, temp_df], ignore_index=True)\n",
    "            bar.update(len(all_items))\n",
    "\n",
    "    result.sort_values(by=[\"user_id\", \"index\"], inplace=True)\n",
    "        \n",
    "    result.reset_index(inplace=True)\n",
    "    # 移除index\n",
    "    result.drop(columns=[\"index\", \"level_0\"], inplace=True)\n",
    "    result.to_csv(expose, index=False)\n",
    "    print(f\"Finish {dataset}\")"
   ]
  }
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